1. Field of the Invention
The present invention relates to integrated circuit design and fabrication, and more particularly to methods for predicting positional information about a feature in an integrated circuit layout.
2. Description of Related Art
In photolithography, features are created on an integrated circuit or other device by exposing a mask pattern (As used herein, the term “mask” includes the term “reticle”) to project an image onto a wafer that is coated with light sensitive materials. After exposure, the wafer is chemically and mechanically processed to create the features defined by the exposure pattern.
As the features created on a wafer become increasingly small, optical and other process distortions occur in which the desired features to be created on a wafer do not match the actual features created on the wafer. Examples of deviations include corner-rounding, line-end shortening, etc., which can significantly degrade the functional performance of the desired features. To compensate for the distortions, many photolithographic processes use one or more resolution enhancement techniques (RET) to improve the pattern fidelity with which the desired pattern is printed on the wafer.
In most resolution enhancement techniques such as optical and proximity correction (OPC), a simulation is made of how a feature will print on a wafer. The simulation is then used to adjust a pattern contained on a mask or reticle in a way that compensates for the expected distortions. Despite the name, OPC typically also includes pre-correction of other undesirable pattern distortions unrelated to the optical image transfer such as those caused by mask manufacturing and etching processes.
As part of the simulation, a resist model can be used that predicts how the resist materials will behave when exposed with a particular mask pattern.
OPC resist models are typically based on a “threshold model” which makes the approximation that any point at a fixed depth in the resist that receives an incident amount of energy above some threshold value will either develop away, or remain, depending on whether the photoresist is positive or negative. These resist models include “variable threshold models” in which the model threshold value depends upon the environment of the feature.
It has been observed that current OPC modeling methods are accurate at predicting the effects of optics, but have only fair accuracy at predicting resist behavior because complex resist behavior including effects such as vertical diffusion, development rate effects, resist thickness impact, inhibition layer impact, etc. cannot be accurately accounted for in the “threshold” resist models.
Thus in calibration the optical model parameters are often distorted to match the behavior of the resist response, which lowers model predictability and is very computationally and human resource inefficient.
It is therefore desirable to provide more accurate methods for modeling resist behavior which are computationally efficient and can be used for use with OPC.